Image data of the vehicle's surroundings, supplied by cameras, are required for a great many different driver assistance systems in vehicles. These image data aid in staying in lane (LDW or lane departure warning), to warn the driver for example that he/she is becoming excessively tired; in automatic lane monitoring (LKS or lane keeping support), for example as part of an automatic vehicle steering system; in recognizing traffic signs (RSR, road sign recognition); or with active night vision support with object recognition and warning function. The image data for night vision support are provided by cameras that are capable of night vision.
All systems that have a camera-based active recognition function based on image data of the vehicle's surroundings have in common that they are very sensitive to the quality of the available images. Recognizing road boundaries or center lines, as well as recognizing traffic signs, requires a high-quality image from the driver's perspective. Since the cameras used for this purpose are mostly located in the upper center of the windshield, or else in specially situated cavities of the vehicle, the cameras do not have access to a better view through the windshield than the driver does. Consequently, the performance of these systems drops off considerably during rain, because the optical path of the cameras is severely impaired, and therefore center lines can either no longer be recognized at all or only erratically, and road signs cannot be detected at all.
In particular the images of the vehicle's surroundings during rain through the rain-dampened windshield, in some cases severely distorted, enable the above-named driver assistance systems to analyze the recorded image only inadequately or not at all, or raindrops on the windshield act like local lenses and blind the camera so severely that it can no longer record usable images.
Techniques are discussed in DE 102 19 788 for interpreting the image information by evaluating the contrast relationship to an extent where it is possible to determine the effective visual range, so that the complete unit is able to separate usable image information from unusable image information. An actual improvement of the driver assistance function cannot be achieved by this technique, however.